{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": { "id": "qBYcPYAb059g" }, "outputs": [], "source": [ "# =========================\n", "# 1. IMPORT LIBRARY\n", "# =========================\n", "import re\n", "import pandas as pd\n", "\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "mo-yt5Ob1N8j" }, "outputs": [], "source": [ "# =========================\n", "# 2. DATA TEKS MANUAL\n", "# =========================\n", "documents = [\n", " \"saya suka belajar data science\",\n", " \"machine learning sangat menarik\",\n", " \"saya tidak suka matematika\",\n", " \"belajar python itu menyenangkan\",\n", " \"data science membutuhkan matematika\",\n", " \"python sangat membantu data science\"\n", "]\n", "\n", "labels = [\n", " \"positif\",\n", " \"positif\",\n", " \"negatif\",\n", " \"positif\",\n", " \"netral\",\n", " \"positif\"\n", "]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "FkmxRAFq1oDK" }, "outputs": [], "source": [ "# =========================\n", "# 3. PREPROCESSING\n", "# =========================\n", "def clean_text(text):\n", " text = text.lower()\n", " text = re.sub(r\"[^a-z\\s]\", \"\", text)\n", " text = re.sub(r\"\\s+\", \" \", text).strip()\n", " return text\n", "\n", "documents = [clean_text(doc) for doc in documents]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ybC1Vo2C_c3q", "outputId": "1695b30d-059d-4bce-e224-7c100b1958ee" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=== Bag of Words ===\n", " belajar data itu learning machine matematika membantu membutuhkan \\\n", "0 1 1 0 0 0 0 0 0 \n", "1 0 0 0 1 1 0 0 0 \n", "2 0 0 0 0 0 1 0 0 \n", "3 1 0 1 0 0 0 0 0 \n", "4 0 1 0 0 0 1 0 1 \n", "5 0 1 0 0 0 0 1 0 \n", "\n", " menarik menyenangkan python sangat saya science suka tidak \n", "0 0 0 0 0 1 1 1 0 \n", "1 1 0 0 1 0 0 0 0 \n", "2 0 0 0 0 1 0 1 1 \n", "3 0 1 1 0 0 0 0 0 \n", "4 0 0 0 0 0 1 0 0 \n", "5 0 0 1 1 0 1 0 0 \n" ] } ], "source": [ "# =========================\n", "# 4. FEATURE EXTRACTION (BOW)\n", "# =========================\n", "vectorizer = CountVectorizer()\n", "X = vectorizer.fit_transform(documents)\n", "\n", "df_bow = pd.DataFrame(\n", " X.toarray(),\n", " columns=vectorizer.get_feature_names_out()\n", ")\n", "\n", "print(\"=== Bag of Words ===\")\n", "print(df_bow)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 80 }, "id": "s6S-Ma4R1xuq", "outputId": "f65359e3-bb87-42b4-fb37-9c035f88e7ad" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "MultinomialNB()" ], "text/html": [ "
MultinomialNB()
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" ] }, "metadata": {}, "execution_count": 15 } ], "source": [ "# =========================\n", "# 5. MODEL KLASIFIKASI\n", "# =========================\n", "model = MultinomialNB()\n", "model.fit(X, labels)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "ShevCTva2Fg9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "228e8f03-bcfb-4ecc-c36f-d86402530a5b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Kalimat uji : saya suka belajar python\n", "Hasil klasifikasi : positif\n" ] } ], "source": [ "# =========================\n", "# 6. PREDIKSI DATA BARU\n", "# =========================\n", "test_sentence = [\"saya suka belajar python\"]\n", "test_sentence = [clean_text(test_sentence[0])]\n", "\n", "X_test = vectorizer.transform(test_sentence)\n", "prediction = model.predict(X_test)\n", "\n", "print(\"\\nKalimat uji :\", test_sentence[0])\n", "print(\"Hasil klasifikasi :\", prediction[0])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "NQjExannHuj0" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 0 }